TY - GEN
T1 - Acceptance factors for using a big data capability and maturity model
AU - Saltz, Jeffrey S.
N1 - Publisher Copyright:
© 2017 Proceedings of the 25th European Conference on Information Systems, ECIS 2017. All rights reserved.
PY - 2017
Y1 - 2017
N2 - Big data is an emerging field that combines expertise across a range of domains, including software development, data management and statistics. However, it has been shown that big data projects suffer because they often operate at a low level of process maturity. To help address this gap, the Diffusion of Innovation Theory is used as a theoretical lens to identify factors that might drive an organization to try and improve their process maturity. Specifically, thirteen acceptance factors for teams to use (or not use) a Big Data CMM are identified. These results suggest that a positive perception exists with respect to relative advantage, compatibility and observability factors, and a negative perception exists with respect to perceived complexity. While more work is required to refine the list of factors, this insight can help guide the improvement of big data team processes.
AB - Big data is an emerging field that combines expertise across a range of domains, including software development, data management and statistics. However, it has been shown that big data projects suffer because they often operate at a low level of process maturity. To help address this gap, the Diffusion of Innovation Theory is used as a theoretical lens to identify factors that might drive an organization to try and improve their process maturity. Specifically, thirteen acceptance factors for teams to use (or not use) a Big Data CMM are identified. These results suggest that a positive perception exists with respect to relative advantage, compatibility and observability factors, and a negative perception exists with respect to perceived complexity. While more work is required to refine the list of factors, this insight can help guide the improvement of big data team processes.
KW - Big Data
KW - Data Science
KW - Project Management
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M3 - Conference contribution
AN - SCOPUS:85054209023
T3 - Proceedings of the 25th European Conference on Information Systems, ECIS 2017
SP - 2602
EP - 2612
BT - Proceedings of the 25th European Conference on Information Systems, ECIS 2017
PB - Association for Information Systems
T2 - 25th European Conference on Information Systems, ECIS 2017
Y2 - 5 June 2017 through 10 June 2017
ER -